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pro vyhledávání: '"SANDERSON, MARK"'
With the rise of Large Language Models (LLMs) such as ChatGPT, researchers have been working on how to utilize the LLMs for better recommendations. However, although LLMs exhibit black-box and probabilistic characteristics (meaning their internal wor
Externí odkaz:
http://arxiv.org/abs/2411.12121
We propose Counterfactual Analysis Quadratic Unconstrained Binary Optimization (CAQUBO) to solve QUBO problems for feature selection in recommender systems. CAQUBO leverages counterfactual analysis to measure the impact of individual features and fea
Externí odkaz:
http://arxiv.org/abs/2410.15272
Algorithmic recourse provides actions to individuals who have been adversely affected by automated decision-making and helps them achieve a desired outcome. Knowing the recourse, however, does not guarantee that users would implement it perfectly, ei
Externí odkaz:
http://arxiv.org/abs/2410.02273
This paper is a draft of a chapter intended to appear in a forthcoming book on generative information retrieval, co-edited by Chirag Shah and Ryen White. In this chapter, we consider generative information retrieval evaluation from two distinct but i
Externí odkaz:
http://arxiv.org/abs/2404.08137
The effectiveness of clarification question models in engaging users within search systems is currently constrained, casting doubt on their overall usefulness. To improve the performance of these models, it is crucial to employ assessment approaches
Externí odkaz:
http://arxiv.org/abs/2403.09180
Publikováno v:
International Journal of Human-Computer Studies (IJHCS), 2024, 103376, ISSN 1071-5819
Automated decision-making systems are becoming increasingly ubiquitous, which creates an immediate need for their interpretability and explainability. However, it remains unclear whether users know what insights an explanation offers and, more import
Externí odkaz:
http://arxiv.org/abs/2309.08438
Autor:
Hettiachchi, Danula, Holcombe-James, Indigo, Livingstone, Stephanie, de Silva, Anjalee, Lease, Matthew, Salim, Flora D., Sanderson, Mark
Crowdsourced annotation is vital to both collecting labelled data to train and test automated content moderation systems and to support human-in-the-loop review of system decisions. However, annotation tasks such as judging hate speech are subjective
Externí odkaz:
http://arxiv.org/abs/2309.01288
Autor:
Trisedya, Bayu Distiawan, Salim, Flora D, Chan, Jeffrey, Spina, Damiano, Scholer, Falk, Sanderson, Mark
Knowledge graphs (KGs) are becoming essential resources for many downstream applications. However, their incompleteness may limit their potential. Thus, continuous curation is needed to mitigate this problem. One of the strategies to address this pro
Externí odkaz:
http://arxiv.org/abs/2308.13755
Autor:
Hettiachchi, Danula, Ji, Kaixin, Kennedy, Jenny, McCosker, Anthony, Salim, Flora D., Sanderson, Mark, Scholer, Falk, Spina, Damiano
With the rapid growth of online misinformation, it is crucial to have reliable fact-checking methods. Recent research on finding check-worthy claims and automated fact-checking have made significant advancements. However, limited guidance exists rega
Externí odkaz:
http://arxiv.org/abs/2308.10220
Micro-mobility services (e.g., e-bikes, e-scooters) are increasingly popular among urban communities, being a flexible transport option that brings both opportunities and challenges. As a growing mode of transportation, insights gained from micro-mob
Externí odkaz:
http://arxiv.org/abs/2304.08721